Efforts to mitigate global climate change necessitate the establishment of regional and local carbon emission control targets and reduction plans by countries, regions, cities, and companies. The scaling nature of carbon emissions has received limited attention when viewed through the lens of human activities. Most efforts to reduce carbon emissions grapple with uncertainties, such as the relationship between CO2 emissions and the overall volume of traffic, often overlooking the significance of traffic scale. The common assumption that "increased traffic leads to higher CO2 emissions" has seldom been rigorously examined and quantified within the context of urban environments.
Assistant Professor Di Zhu (Geography, Environment, and Society; MSI PI) is working on a project called “Quantifying Carbon Emission Efficiency through the Lens of Human Mobility,” which delves into Carbon Emission Efficiency (CEE) through the lens of human mobility in major U.S. cities. With urban populations projected to reach 6.3 billion by 2050, understanding CEE is paramount for sustainable urban development and climate action. The project employs a novel approach, combining urban scaling, data mining, and geospatial AI, to analyze the intricate relationship between human activities, transportation systems, and carbon emissions. The goal is to establish a national benchmark for carbon emissions evaluation, essential for effective climate regulation. The project unfolds in three phases: developing core models for scaling analysis, applying these models to multi-city and long-term observations, and exploring spatiotemporal CEE patterns across major U.S. cities. The project does not just report emissions; it also deciphers the complex relationship between transportation systems and the climate. This will provide policymakers with data-driven insights for effective climate policies, support sustainable transportation planning, and improve urban well-being.
This project recently received a DSI Seed Grant. The Seed Grant program is intended to promote, catalyze, accelerate, and advance U of M-based data science research so that U of M faculty and staff are well prepared to compete for longer term external funding opportunities.
The program was updated in Summer 2024 to include three focus areas: Foundational Data Sciences; Digital Health and Personalized Health Care Delivery; and Agriculture and the Environment. The types of awards are Rapid Response Grants and new types, Awards for DSI Faculty Fellowship and Data Sets (Data as an Asset).
This project falls under the Foundational Data Sciences focus area.
Image description: Illustrative maps of human mobility flows in the Twin Cities metropolitan area, Minnesota (left), and ground transportation CO2 emissions in the coterminous U.S. (right). The mobility flows map (left) is derived from daily individual mobile positioning records collected by this research team. The CO2 emissions map (right) is adopted from the 2021 global gridded daily CO2 emissions dataset (GRACED).
